Integrating Balanced Scorecard and Fuzzy TOPSIS for Innovation Performance Evaluation

Abstract

Innovation performance measurement is challenging due to the complexity and multidimensionality of the innovation processes. In fact, it is difficult to recognize what and how it should be measured. A solution can be the use of Balanced Scorecard (BSC) method that is easily adaptable to the needs of the organization in any of its strategic areas. However, the comprehensive measurement of innovation performance is also associated with a high level of uncertainty. This is mainly due to the fact that measurement methods are very often based on respondents' opinions. Hence, fuzzy set-based approaches are appropriate for this evaluation. Fuzzy TOPSIS is reported to be a reliable method used for multi-criteria decision making, where reference is made to a preferred/non-preferred alternative. This method allows not only taking into account the uncertainty present in the evaluation of innovation performance, but it also allows comparison and ranking of companies both within and among the different branches of industry. Hence, we propose an approach for innovation performance evaluation that integrates BSC and fuzzy TOPSIS. Empirical experiments are carried out on a large data set of European companies and the results are verified by the division of companies into knowledge intensive and high-tech industries. Further, the results are compared with exploratory factor analysis, the traditional statistical method used to evaluate innovation performance.